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From Robert Stewart <>
Subject Re: Clustering a large crawl
Date Wed, 30 May 2012 20:11:55 GMT
That is a good point.   t1/t2 are distance measures but cosine is a similarity measure, so
you need to think of it as 1-cosine.

On May 30, 2012, at 4:03 PM, Jeff Eastman wrote:

> Have you tried much smaller values for t1=t2? Recall that the t-values specify the distance
within which a new point is assigned to an existing canopy. In the limit as t -> 0, you
should get n clusters, where n is the number of documents in your corpus.
> On 5/30/12 1:23 PM, Pat Ferrel wrote:
>> I have about 150,000 docs on which I ran canopy with values for t1 = t2 from 0.1
to 0.95 using the Cosine distance measure. I got results that range from 1.5 docs per cluster
to 3. In other words canopy produced a very large number of centroids, which does not seem
to represent the data very well. Trying random values for k seems to produce better results
but still spotty and hard to judge. I am at the point of giving up on canopy and so wrote
a utility to simply iterate k over some values and run the evaluators each time, but there
are currently some problems with CDbw (Inter-Cluster Density is always 0.0 for instance).
>> This seems like such a fundamental problem that others must have found a way to get
better results. Any suggestions?

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